[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124761-en":3,"doc-seo-124761-105":30,"detail-sidebar-cat-0-en-105":95},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},124761,1649267921044,"Ava Thompson","https://us-avatar.wpscdn.com/avatar/1800007509477c92dfb?_k=1782875107921204101",8,"Research & Report","Designing Optimal Behavioral Experiments Using Machine Learning","Computational models help clarify theories of cognition and behavior through precise expression and predictive power, yet they make traditional experimental design feel unintuitive and prone to bias. Bayesian optimal experimental design (BOED) addresses this by selecting experiments expected to be maximally informative for discriminating models and their auxiliary assumptions. This tutorial combines recent BOED advances with machine learning to design optimal experiments for any simulatable model, supports model and parameter evaluation via by-products, and demonstrates performance on bandit exploration–exploitation through simulations and a real study. Practical caveats are discussed.","Edinburgh Research Explorer  \nDesigning Optimal Behavioral Experiments Using Machine Learning  \nCitation for published version:  \nValentin, S, Kleinegesse, S, Bramley, NR, Seriès, P, Gutmann, MU & Lucas, CG 2024, 'Designing Optimal Behavioral Experiments Using Machine Learning', eLIFE, vol. 13, e86224, pp. 1-40.  \n[https://doi.org/10.7554/eLife.86224](https://doi.org/10.7554/eLife.86224)  \nDigital Object Identifier (DOI):  \n10.7554/eLife.86224  \nLink:  \nLink to publication record in Edinburgh Research Explorer  \nDocument Version:  \nPublisher's PDF, also known as Version of record  \nPublished In:  \neLIFE  \nGeneral rights  \nCopyright for the publications made accessible via the Edinburgh Research Explorer is retained by the author(s) and / or other copyright owners and it is a condition of accessing these publications that users recognise and abide by the legal requirements associated with these rights.  \nTake down policy  \nThe University of Edinburgh has made every reasonable effort to ensure that Edinburgh Research Explorer content complies with UK legislation. If you believe that the public display of this file breaches copyright please [contact openaccess@ed.ac.uk](contact openaccess@ed.ac.uk) providing details, and we will remove access to the work immediately and investigate your claim.  \nDownload date: 11. Feb. 2024  \nREVIEw ARtIcLE  \n*For correspondence:  \n[simonvalentin@me.com](simonvalentin@me.com)[ ](simonvalentin@me.com)†These authors contributed equally to this work  \nCompeting interest: The authors declare that no competing interests exist.  \nFunding: See page 23  \nReceived: 17 January 2023  \nPreprinted: 12 May 2023  \nAccepted: 19 November 2023  \nPublished: 23 January 2024  \nReviewing Editor: Joshua I Gold, University of Pennsylvania, United States  \n Copyright Valentin, Kleinegesse et al. This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use andredistribution provided that the original author and source are credited.  \nDesigning optimal behavioral experiments using machine learning  \nSimon Valentin1*†, Steven Kleinegesse1†, Neil R Bramley2, Peggy Seriès1, Michael U Gutmann1, Christopher G Lucas1  \n1School of Informatics, University of Edinburgh, Edinburgh, United Kingdom; 2 Department of Psychology, University of Edinburgh, Edinburgh, United Kingdom  \nAbstract Computational models are powerful tools for understanding human cognition and behavior. They let us express our theories clearly and precisely and offer predictions that can be subtle and often counter-intuitive. However, this same richness and ability to surprise means our scientific intuitions and traditional tools are ill-suited to designing experiments to test and compare these models. To avoid these pitfalls and realize the full potential of computational modeling, we require tools to design experiments that provide clear answers about what models explain human behavior and the auxiliary assumptions those models must make. Bayesian optimal experimental design (BOED) formalizes the search for optimal experimental designs by identifying experiments that are expected to yield informative data. In this work, we provide a tutorial on leveraging recent advances in BOED and machine learning to find optimal experiments for any kind of model that we can simulate data from, and show how by-products of this procedure allow for quick and straightforward evaluation of models and their parameters against real experimental data. As a case study, we consider theories of how people balance exploration and exploitation in multi-armed bandit decision-making tasks. We validate the presented approach using simulations and a real-world experiment. As compared to experimental designs commonly used in the literature, we show that our optimal designs more efficiently determine which of a set of models best account for individual human behavior, and more efficiently characterize behavior given a preferred model. 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used as a case study?\",\"answer\":\"The paper studies theories of how people balance exploration and exploitation in multi-armed bandit decision-making tasks.\"},{\"question\":\"How are the proposed methods validated and what is the reported benefit?\",\"answer\":\"They are validated using simulations and a real-world experiment, showing that optimal designs more efficiently identify the best model set and characterize behavior under a preferred model.\"}]","Designing Optimal Behavioral Experiments Using Machine Learning | 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